| Type: | Package |
| Title: | Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy |
| Version: | 0.2.21 |
| Description: | Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette. |
| License: | GPL-3 |
| URL: | https://github.com/BZPaper/RTransferEntropy |
| BugReports: | https://github.com/BZPaper/RTransferEntropy/issues |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.1.2) |
| Imports: | future (≥ 1.19.0), future.apply, Rcpp |
| LazyData: | true |
| RoxygenNote: | 7.2.0 |
| LinkingTo: | Rcpp |
| Suggests: | data.table, ggplot2, gridExtra, knitr, quantmod, rmarkdown, testthat, vars, xts, zoo |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2023-02-01 16:53:12 UTC; david |
| Author: | David Zimmermann [aut, cre], Simon Behrendt [aut], Thomas Dimpfl [aut], Franziska Peter [aut] |
| Maintainer: | David Zimmermann <david_j_zimmermann@hotmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-02-01 17:30:05 UTC |
Calculates the Effective Transfer Entropy for two time series
Description
Calculates the Effective Transfer Entropy for two time series
Usage
calc_ete(
x,
y,
lx = 1,
ly = 1,
q = 0.1,
entropy = "Shannon",
shuffles = 100,
type = "quantiles",
quantiles = c(5, 95),
bins = NULL,
limits = NULL,
burn = 50,
seed = NULL,
na.rm = TRUE
)
Arguments
x |
a vector of numeric values, ordered by time.
Also allowed are |
y |
a vector of numeric values, ordered by time.
Also allowed are |
lx |
Markov order of x, i.e. the number of lagged values affecting the
current value of x. Default is |
ly |
Markov order of y, i.e. the number of lagged values affecting the
current value of y. Default is |
q |
a weighting parameter used to estimate Renyi transfer entropy,
parameter is between 0 and 1. For |
entropy |
specifies the transfer entropy measure that is estimated,
either 'Shannon' or 'Renyi'. The first character can be used
to specify the type of transfer entropy as well. Default is
|
shuffles |
the number of shuffles used to calculate the effective
transfer entropy. Default is |
type |
specifies the type of discretization applied to the observed time
series:'quantiles', 'bins' or 'limits'. Default is
|
quantiles |
specifies the quantiles of the empirical distribution of the
respective time series used for discretization.
Default is |
bins |
specifies the number of bins with equal width used for
discretization. Default is |
limits |
specifies the limits on values used for discretization.
Default is |
burn |
the number of observations that are dropped from the beginning of
the bootstrapped Markov chain. Default is |
seed |
a seed that seeds the PRNG (will internally just call set.seed),
default is |
na.rm |
if missing values should be removed (will remove the values at
the same point in the other series as well). Default is |
Value
a single numerical value for the effective transfer entropy
See Also
Examples
# construct two time-series
set.seed(1234567890)
n <- 1000
x <- rep(0, n + 1)
y <- rep(0, n + 1)
for (i in seq(n)) {
x[i + 1] <- 0.2 * x[i] + rnorm(1, 0, 2)
y[i + 1] <- x[i] + rnorm(1, 0, 2)
}
x <- x[-1]
y <- y[-1]
# calculate the X->Y transfer entropy value
calc_ete(x, y)
# calculate the Y->X transfer entropy value
calc_ete(y, x)
# Compare the results
# even with the same seed, transfer_entropy might return slightly different
# results from calc_ete
calc_ete(x, y, seed = 123)
calc_ete(y, x, seed = 123)
transfer_entropy(x, y, nboot = 0, seed = 123)
Calculates the Transfer Entropy for two time series
Description
Calculates the Transfer Entropy for two time series
Usage
calc_te(
x,
y,
lx = 1,
ly = 1,
q = 0.1,
entropy = "Shannon",
shuffles = 100,
type = "quantiles",
quantiles = c(5, 95),
bins = NULL,
limits = NULL,
burn = 50,
seed = NULL,
na.rm = TRUE
)
Arguments
x |
a vector of numeric values, ordered by time.
Also allowed are |
y |
a vector of numeric values, ordered by time.
Also allowed are |
lx |
Markov order of x, i.e. the number of lagged values affecting the
current value of x. Default is |
ly |
Markov order of y, i.e. the number of lagged values affecting the
current value of y. Default is |
q |
a weighting parameter used to estimate Renyi transfer entropy,
parameter is between 0 and 1. For |
entropy |
specifies the transfer entropy measure that is estimated,
either 'Shannon' or 'Renyi'. The first character can be used
to specify the type of transfer entropy as well. Default is
|
shuffles |
the number of shuffles used to calculate the effective
transfer entropy. Default is |
type |
specifies the type of discretization applied to the observed time
series:'quantiles', 'bins' or 'limits'. Default is
|
quantiles |
specifies the quantiles of the empirical distribution of the
respective time series used for discretization.
Default is |
bins |
specifies the number of bins with equal width used for
discretization. Default is |
limits |
specifies the limits on values used for discretization.
Default is |
burn |
the number of observations that are dropped from the beginning of
the bootstrapped Markov chain. Default is |
seed |
a seed that seeds the PRNG (will internally just call set.seed),
default is |
na.rm |
if missing values should be removed (will remove the values at
the same point in the other series as well). Default is |
Value
a single numerical value for the transfer entropy
See Also
Examples
# construct two time-series
set.seed(1234567890)
n <- 1000
x <- rep(0, n + 1)
y <- rep(0, n + 1)
for (i in seq(n)) {
x[i + 1] <- 0.2 * x[i] + rnorm(1, 0, 2)
y[i + 1] <- x[i] + rnorm(1, 0, 2)
}
x <- x[-1]
y <- y[-1]
# calculate the X->Y transfer entropy value
calc_te(x, y)
# calculate the Y->X transfer entropy value
calc_te(y, x)
# Compare the results
calc_te(x, y, seed = 123)
calc_te(y, x, seed = 123)
transfer_entropy(x, y, nboot = 0, seed = 123)
Extract the Coefficient Matrix from a transfer_entropy
Description
Extract the Coefficient Matrix from a transfer_entropy
Usage
## S3 method for class 'transfer_entropy'
coef(object, ...)
Arguments
object |
a transfer_entropy |
... |
additional arguments, currently not in use |
Value
a Matrix containing the coefficients
Examples
set.seed(1234567890)
n <- 500
x <- rep(0, n + 1)
y <- rep(0, n + 1)
for (i in seq(n)) {
x[i + 1] <- 0.2 * x[i] + rnorm(1, 0, 2)
y[i + 1] <- x[i] + rnorm(1, 0, 2)
}
x <- x[-1]
y <- y[-1]
te_result <- transfer_entropy(x, y, nboot = 100)
coef(te_result)
Checks if an object is a transfer_entropy
Description
Checks if an object is a transfer_entropy
Usage
is.transfer_entropy(x)
Arguments
x |
an object |
Value
a boolean value if x is a transfer_entropy
Examples
# see ?transfer_entropy
Prints a transfer-entropy result
Description
Prints a transfer-entropy result
Usage
## S3 method for class 'transfer_entropy'
print(
x,
digits = 4,
boot = TRUE,
probs = c(0, 0.25, 0.5, 0.75, 1),
tex = FALSE,
ref = NA,
file = NA,
table = TRUE,
...
)
Arguments
x |
a transfer_entropy |
digits |
the number of digits to display, defaults to 4 |
boot |
if the bootstrapped results should be printed, defaults to TRUE |
probs |
numeric vector of quantiles for the bootstraps |
tex |
if the data should be outputted as a TeX-string |
ref |
the reference string of the LaTeX table (label) applies only if table = TRUE and tex = TRUE, defaults to FALSE |
file |
a file where the results are printed to |
table |
if the table environment should be printed as well (only applies if tex = TRUE), defaults to TRUE |
... |
additional arguments, currently not in use |
Value
invisible the text
Examples
# construct two time-series
set.seed(1234567890)
n <- 500
x <- rep(0, n + 1)
y <- rep(0, n + 1)
for (i in seq(n)) {
x[i + 1] <- 0.2 * x[i] + rnorm(1, 0, 2)
y[i + 1] <- x[i] + rnorm(1, 0, 2)
}
x <- x[-1]
y <- y[-1]
# Calculate Shannon's Transfer Entropy
te_result <- transfer_entropy(x, y, nboot = 100)
print(te_result)
# change the number of digits
print(te_result, digits = 10)
# disable boot-print
print(te_result, boot = FALSE)
# specify the quantiles of the bootstraps
print(te_result, probs = c(0, 0.1, 0.4, 0.5, 0.6, 0.9, 1))
# get LaTeX output:
print(te_result, tex = TRUE)
# set the reference label for LaTeX table
print(te_result, tex = TRUE, ref = "tab:te_result")
## Not run:
# file output
print(te_result, file = "te_result_file.txt")
print(te_result, tex = TRUE, file = "te_result_file.tex")
## End(Not run)
Set the quiet-parameter for all RTransferEntropy Calls
Description
Set the quiet-parameter for all RTransferEntropy Calls
Usage
set_quiet(quiet)
Arguments
quiet |
if FALSE, the functions will give feedback on the progress |
Value
nothing
Examples
# see ?transfer_entropy
Daily stock data for 10 stocks from 2000-2017
Description
A dataset containing the daily stock returns for 10 stocks and the S&P 500 market returns for the time-period 2000-01-04 until 2017-12-29
Usage
stocks
Format
A data frame (or data.table if loaded) with 46940 rows and 4 variables:
- date
date of the observation
- ticker
ticker of the stock
- ret
Return of the stock
- sp500
Return of the S&P 500 stock market index
Source
yahoo finance using getSymbols
Prints a summary of a transfer-entropy result
Description
Prints a summary of a transfer-entropy result
Usage
## S3 method for class 'transfer_entropy'
summary(object, digits = 4, probs = c(0, 0.25, 0.5, 0.75, 1), ...)
Arguments
object |
a transfer_entropy |
digits |
the number of digits to display, defaults to 4 |
probs |
numeric vector of quantiles for the bootstraps |
... |
additional arguments, passed to |
Value
invisible the object
Examples
# construct two time-series
set.seed(1234567890)
n <- 500
x <- rep(0, n + 1)
y <- rep(0, n + 1)
for (i in seq(n)) {
x[i + 1] <- 0.2 * x[i] + rnorm(1, 0, 2)
y[i + 1] <- x[i] + rnorm(1, 0, 2)
}
x <- x[-1]
y <- y[-1]
# Calculate Shannon's Transfer Entropy
te_result <- transfer_entropy(x, y, nboot = 100)
summary(te_result)
Function to estimate Shannon and Renyi transfer entropy between two time series x and y.
Description
Function to estimate Shannon and Renyi transfer entropy between two time series x and y.
Usage
transfer_entropy(
x,
y,
lx = 1,
ly = 1,
q = 0.1,
entropy = "Shannon",
shuffles = 100,
type = "quantiles",
quantiles = c(5, 95),
bins = NULL,
limits = NULL,
nboot = 300,
burn = 50,
quiet = NULL,
seed = NULL,
na.rm = TRUE
)
Arguments
x |
a vector of numeric values, ordered by time.
Also allowed are |
y |
a vector of numeric values, ordered by time.
Also allowed are |
lx |
Markov order of x, i.e. the number of lagged values affecting the
current value of x. Default is |
ly |
Markov order of y, i.e. the number of lagged values affecting the
current value of y. Default is |
q |
a weighting parameter used to estimate Renyi transfer entropy,
parameter is between 0 and 1. For |
entropy |
specifies the transfer entropy measure that is estimated,
either 'Shannon' or 'Renyi'. The first character can be used
to specify the type of transfer entropy as well. Default is
|
shuffles |
the number of shuffles used to calculate the effective
transfer entropy. Default is |
type |
specifies the type of discretization applied to the observed time
series:'quantiles', 'bins' or 'limits'. Default is
|
quantiles |
specifies the quantiles of the empirical distribution of the
respective time series used for discretization.
Default is |
bins |
specifies the number of bins with equal width used for
discretization. Default is |
limits |
specifies the limits on values used for discretization.
Default is |
nboot |
the number of bootstrap replications for each direction of
the estimated transfer entropy. Default is |
burn |
the number of observations that are dropped from the beginning of
the bootstrapped Markov chain. Default is |
quiet |
if FALSE (default), the function gives feedback. |
seed |
a seed that seeds the PRNG (will internally just call set.seed),
default is |
na.rm |
if missing values should be removed (will remove the values at
the same point in the other series as well). Default is |
Value
an object of class transfer_entropy, containing the transfer entropy
estimates in both directions, the effective transfer entropy
estimates in both directions, standard errors and p-values based on
bootstrap replications of the Markov chains under the null hypothesis
of statistical independence, an indication of statistical
significance, and quantiles of the bootstrap samples
(if nboot > 0).
See Also
Examples
# construct two time-series
set.seed(1234567890)
n <- 500
x <- rep(0, n + 1)
y <- rep(0, n + 1)
for (i in seq(n)) {
x[i + 1] <- 0.2 * x[i] + rnorm(1, 0, 2)
y[i + 1] <- x[i] + rnorm(1, 0, 2)
}
x <- x[-1]
y <- y[-1]
# Calculate Shannon's Transfer Entropy
te_result <- transfer_entropy(x, y, nboot = 100)
te_result
summary(te_result)
# Parallel Processing using the future-package
library(future)
plan(multisession)
te_result2 <- transfer_entropy(x, y, nboot = 100)
te_result2
# revert back to sequential execution
plan(sequential)
te_result2 <- transfer_entropy(x, y, nboot = 100)
te_result2
# General set of quiet
set_quiet(TRUE)
a <- transfer_entropy(x, y, nboot = 0)
set_quiet(FALSE)
a <- transfer_entropy(x, y, nboot = 0)
# close multisession, see also ?plan
plan(sequential)